Outcomes of Children With Low-Grade Gliomas in Low- and Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Pediatric CNS tumors are increasingly a priority, particularly with the WHO designation of low-grade glioma (LGG) as one of six index childhood cancers. There are currently limited data on outcomes of pediatric patients with LGGs in low- and middle-income countries (LMICs). METHODS: To better understand the outcomes of LGGs in LMICs, this systematic review interrogated nine literature databases. RESULTS: The search identified 14,977 publications. Sixteen studies from 19 countries met the selection criteria and were included for data abstraction and analysis. Eleven studies (69%) were retrospective reviews from single institutions, and one (6%) captured institutional data prospectively. The studies captured a total of 957 patients with a median of 49 patients per study. Seven (44%) of the studies described the treatment modalities used. Of 373 patients for whom there was information, 173 (46%) had a gross total or near total resection, 109 (29%) had a subtotal resection, and 91 (24%) had only a biopsy performed. Seven studies, with a total of 476 patients, described the frequency of use of radiotherapy and/or chemotherapy in the cohorts: 83 of these patients received radiotherapy and 76 received chemotherapy. The 5-year overall survival ranged from 69.2% to 93.5%, although lower survival rates were reported at earlier time points. We identified limitations in the published studies with respect to the cohort sizes and methodologies. CONCLUSION: The included studies reported survival rates frequently exceeding 80%, although the ultimate number of studies was limited, pointing to the paucity of studies describing the outcomes of children with LGGs in LMICs. This study underscores the need for more robust data on outcomes in pediatric LGG.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".